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IROS 2018 Workshop

Room 2.L5 KUKA | IROS 2018

Workshop Objectives:

Outside of the classic industrial environments, robots are not expected to operate in the vacuum and isolated from people. Robots are instead performing their tasks close to and in collaboration with or assistance of humans; be this delivery drones, self-driving cars, robot assisted care or collaborative industrial settings. We take inspiration from us humans: We interact in a perception-action loop, where actions have consequences and thus require prediction and reactions. This requires developing a meaningful model of the humans we are interacting with from observations and interactions. Thus, learning is the glue that enables us to link desired outcomes with purposeful actions; and in a digital, data-driven world, machine learning is at the core. We will explore in this workshop, using cross-disciplinary work, how to close-the-loop of modelling and predicting human interactions as well as human-robot interactions.
A major challenge in analyzing such behavior is to discover some underlying simplicity in a complex and highly variable stream of behavioral actions. The gain of such an analysis is that the underlying simplicity is often a reflection of the mechanism driving behavior. We believe that advanced statistical and probabilistic methods can be used to analyse the unconstrained natural statistics of behaviour. Similar methods need to be applied to robotic systems, understanding their behaviour in conjunction and interaction with the human, to ensure compatibility and complementarity within the human-robot interaction, which is in this case, happening as a closed-loop.
This workshop aims to create a forum for experts in robotics, machine learning, human behaviour analytics, computational neuroscientists, and all relevant stakeholders within academia and industry, on how the current research within these different fields can be brought together to create a new paradigm for human/robot in-the-loop machine learning. To this end, speakers have been selected with care, bringing in top experts and representatives from the above in a unique environment for joint discussions and understanding across the different platforms of research. We hope that this will lead to a new community of cross-disciplinary research, with further workshops of the same topic organised for future and further collaboration.

The above papers will be presented in digital interactive poster format, along with a teaser presentation, at our workshop.

Invited Speakers:

Peter Battaglia, Google DeepMind

Aude Billard, École Polytechnique Fédérale de Lausanne (EPFL)

Haitham Bou-Ammar, PROWLER.io

Daniel Braun, University of Ulm (UULM)

Sylvain Calinon, Idiap Research Institute

Ohad Dan, The Hebrew University of Jerusalem

Aaron Dollar, Yale University

Anca Dragan, University of California, Berkeley

Moritz Grosse-Wentrup, Max Planck Institute for Intelligent Systems

Sami Haddadin, Technical University of Munich (TUM)

Sethu Vijayakumar, University of Edinburgh [tentative]

Tamar Makin, University College London

Franzi Meier, Max Planck Institute for Intelligent Systems

Igor Mordatch, OpenAI

Gerhard Neumann, University of Lincoln

Domenico Prattichizzo, Università di Siena

Francisco Valero-Cuevas, University of Southern California

Jeremy Wyatt, University of Birmingham

Organisers:

Ali Shafti, Imperial College London

Roberto Calandra, UC Berkeley

Marc Deisenroth, Imperial College London

Aldo Faisal, Imperial College London

Topics of Interest:

Machine learning

Human learning

Reinforcement learning

Computational Neuroscience

Neurorobotics

Multi-agent systems

Multi-agent reinforcement learning

Deep reinforcement learning

Human-in-the-loop machine learning

Robot-in-the-loop machine learning

Human-robot interaction

Human-robot collaboration

Intended Audience:

IROS 2018 is expected to cover the main topics within robotics research; as highlighted on the conference website: Human-robot interaction, humanoids, social robots, autonomous systems and, intelligent perception, as well as social aspects of robotics. While researchers in these fields are incorporating machine learning methods in their work, there is no specific session within IROS that is focused on how machine learning can enhance robotics research. In particular, our workshop’s focus on human and robot in-the-loop techniques, makes the discussions interesting to both the robotics and the machine learning community. Our speakers are top researchers from these fields, providing the opportunity for fruitful discussions on how the communities can come together for better collaboration and research output.

We are proudly endorsed by the following IEEE RAS Technical Committees: